An Effective Vector-guided Path Planning Technique for Autonomous Mobile Robots

Luo, Chaomin, Mohan Krinisnan, and Mark Paulik

A wave propagation neural networks approach associated with vector-based guidance of autonomous robot navigation is proposed in this paper.   A wave propagation neural networks (WPNN) algorithm is employed to guide an autonomous robot to reach goal with obstacle avoidance.  As the robot plans its trajectory toward the goal, unreasonable path will be inevitably planned.  A vector-based guidance paradigm is developed for guidance of the robot locally so as to plan more reasonable trajectories. The model algorithm is computationally efficient. It is evident that the proposed model is not very sensitive to the model parameters. In this paper, simulation and comparison results for an intelligent vehicle of the proposed approach with the square-cell-map-based path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free trajectory in unknown environments.